We develop a simulation tool to support policy-decisions about healthcare for
chronic diseases in defined populations. Incident disease-cases are generated
in-silico from an age-sex characterised general population using standard
epidemiological approaches. A novel disease-treatment model then simulates
continuous life courses for each patient using discrete event simulation.
Ideally, the discrete event simulation model would be inferred from complete
longitudinal healthcare data via a likelihood or Bayesian approach. Such data
is seldom available for relevant populations, therefore an innovative approach
to evidence synthesis is required. We propose a novel entropy-based approach to
fit survival densities. This method provides a fully flexible way to
incorporate the available information, which can be derived from arbitrary
sources. Discrete event simulation then takes place on the fitted model using a
competing hazards framework. The output is then used to help evaluate the
potential impacts of policy options for a given population.